System and method for self-supervised monocular ground-plane extraction

ABSTRACT

Systems and methods for extracting ground plane information directly from monocular images using self-supervised depth networks are disclosed. Self-supervised depth networks are used to generate a three-dimensional reconstruction of observed structures. From this reconstruction the system may generate surface normals. The surface normals can be calculated directly from depth maps in a way that is much less computationally expensive and accurate than surface normals extraction from standard LiDAR data. Surface normals facing substantially the same direction and facing upwards may be determined to reflect a ground plane.

FIELD OF TECHNOLOGY

The present disclosure relates to improving machine vision and learning,and more particularly, to extracting ground plane information frommonocular images.

BACKGROUND

Machine learning provides a basis for the design, programming andoperation of autonomous vehicles. Autonomous and semi-autonomousvehicles may be trained according to environmental and situational dataallowing the vehicle to operate and navigate known and unknowntrajectories. Sensors installed and configured on an ego vehicle, i.e.,an autonomous or semi-autonomous vehicle, provide environmental data toa machine learning system. Monocular cameras can be a cost-effectiveapproach when compared to more complex imaging systems including LiDAR,stereo cameras or the like, however the sensor data from monocularcameras does not explicitly include depth information. Instead, thevehicle implements processing routines that derive depth informationfrom the monocular images.

One of the challenges in machine learning is perceiving thethree-dimensional depth of an environment through the capture oftwo-dimensional images. In mapping a three-dimensional environment for avehicle, for example, a depth system must be able to distinguish aroadway or ground surface from other objects and structures rising fromthe ground surface. Traditional depth systems rely on images frommultiple cameras or LiDAR systems to generate depth maps and extractground plane information to assist vehicle navigation. Such systemsrequire additional hardware, i.e., multiple cameras, or expensive andcomplex resources, i.e., LiDAR, to be able to extract three-dimensionaldata, including ground planes, from captured images.

SUMMARY

Aspects of the present disclosure provide for systems and methods forextracting ground plane information directly from monocular images usingself-supervised depth networks. The self-supervised depth networks areused to generate a three-dimensional reconstruction of the observedstructures. From this reconstruction the system may generate surfacenormals, which are vectors perpendicular to the direction or surfaces ateach location. The surface normals can be calculated directly from depthmaps in a way that is much less computationally expensive and accuratethan surface normals extraction from standard LiDAR data. Surfacenormals facing substantially the same direction and facing upwards maybe determined to reflect a ground plane.

According to one aspect of the present disclosure, a system forgenerating a ground plane of an environment is provided. The system mayinclude one or more processors and a memory communicably coupled to theone or more processors. A depth system may include instructions thatwhen executed by the one or more processors cause the one or moreprocessors to, in response to, to generate a depth map. The depth mapmay be generated by receiving at least one monocular image andprocessing the at least one monocular image according to a depth model.An image module may include instructions that when executed by the oneor more processors cause the one or more processors to define in thedepth map a plurality of polygons, and for each polygon, generate asurface normal. A ground plane may be extracted from the surface normalfor each polygon.

According to another aspect of the disclosure, a method for generating aground plane of an environment is provided. At least one monocular imagemay be received and a depth map may be generated by processing the atleast one monocular image according to a depth model. A plurality ofpolygons may be defined in the depth map and, for each polygon, asurface normal may be generated. A ground plane may be extracted fromthe surface normal for each polygon.

According to another aspect, a non-transitory computer-readable mediumfor generating a ground plane for an environment is provided, includinginstructions that when executed by one or more processors cause the oneor more processors to receive at least one monocular image and generatea depth map by processing the at least one monocular image according toa depth model. A depth map may define a plurality of polygons and foreach polygon, a surface normal may be generated. A ground plane may beextracted from the surface normal for each polygon.

This has outlined, rather broadly, the features and technical advantagesof the present disclosure in order that the detailed description thatfollows may be better understood. Additional features and advantages ofthe present disclosure will be described below. It should be appreciatedby those skilled in the art that this present disclosure may be readilyutilized as a basis for modifying or designing other structures forcarrying out the same purposes of the present disclosure. It should alsobe realized by those skilled in the art that such equivalentconstructions do not depart from the teachings of the present disclosureas set forth in the appended claims. The novel features, which arebelieved to be characteristic of the present disclosure, both as to itsorganization and method of operation, together with further objects andadvantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 is a diagram of a vehicle system according to one aspect of thepresent disclosure.

FIG. 2 is a diagram of a depth system according to one aspect of thepresent disclosure.

FIG. 3 is a conceptual diagram of a three-dimensional environmenttransformed to a depth map with calculated surface normals according toone aspect of the present disclosure.

FIG. 4 depicts a method of extracting a ground plane from a surroundingenvironment using monocular images according to one aspect of thepresent disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for providing a thoroughunderstanding of the various concepts. It will be apparent to thoseskilled in the art, however, that these concepts may be practicedwithout these specific details. In some instances, well-known structuresand components are shown in block diagram form in order to avoidobscuring such concepts.

Aspects of the present disclosure provide for systems and methods forground-plane extraction directly from monocular images usingself-supervised depth networks. The self-supervised depth networks maybe used to generate a three-dimensional reconstruction of observedstructures in an environment surrounding, for example, a vehicle. Fromthis reconstruction, the system may generate surface normals, which arevectors perpendicular to the direction or surfaces at each location. Thesurface normals may be calculated directly from depth maps, a structuredtwo-dimensional re-projected version of a three-dimensional point cloudrepresenting the observed structures, in a way that is computationallyless expensive and more accurate than surface normals extraction fromstandard LiDAR data due to the less dense, or sparser, nature of LiDARdata. According to one aspect, surface normals corresponding to the roadmay be isolated, and by doing so, the system may accurately segment theground-plane from the remaining point cloud. While a single ground planemay be sufficient for many applications, aspects of the presentdisclosure allow for the extraction of multiple planes, which may beuseful in the case of irregular or hilly roads. In the extreme case,individual ground tiles may be estimated, generating a height maprepresenting the terrain of the entire observed environment.

FIG. 1 is a diagram illustrating an example of a hardware implementationfor a vehicle system 100, according to aspects of the presentdisclosure. The vehicle system 100 may be part of a passenger vehicle, acarrier vehicle, or other device. For example, as shown in FIG. 1 , thevehicle system 100 may be a component of a component of an autonomous orsemi-autonomous car 128. Aspects of the present disclosure are notlimited to the vehicle system 100 being a component of the car 128, asother devices, including autonomous and semi-autonomous vehicles andother devices are also contemplated for using the vehicle system 100.

The vehicle system 100 may be implemented with a bus architecture,represented generally by a bus 130. The bus 130 may include any numberof interconnecting buses and bridges depending on the specificapplication of the vehicle system 100 and the overall designconstraints. The bus 130 may link together various circuits includingone or more processors and/or hardware modules, represented by aprocessor 120, a communication module 122, a location module 118, asensor module 102, a locomotion module 126, a planning module 124, and acomputer-readable medium 114. The bus 130 may also link various othercircuits such as timing sources, peripherals, voltage regulators, andpower management circuits, which are well known in the art, andtherefore, will not be described any further.

The vehicle system 100 may include a transceiver 116 coupled to theprocessor 120, the sensor module 102, a depth system 108, thecommunication module 122, the location module 118, the locomotion module126, the planning module 124, and the computer-readable medium 114. Thetransceiver 116 is coupled to an antenna 134. The transceiver 116communicates with various other devices over a transmission medium. Forexample, the transceiver 116 may send and receive commands viatransmissions to and from a server or a remote device, such as remotedevice or server (not shown).

The depth system 108 may include the processor 120 coupled to thecomputer-readable medium 114. The processor 120 may perform processing,including the execution of software stored on the computer-readablemedium 114 providing functionality according to the disclosure. Thesoftware, when executed by the processor 120, causes the vehicle system100 to perform the various functions described for a particular device,such as car 128, or any of the modules 102, 108, 114, 116, 118, 120,122, 124, 126. The computer-readable medium 114 may also be used forstoring data that is manipulated by the processor 120 when executing thesoftware.

The sensor module 102 may be used to obtain measurements via differentsensors, such as a first sensor 104, a second sensor 106. The firstsensor 104 may be a motion sensor, such as an accelerometer, gyroscope,inertial measurement unit, or the like. The second sensor 106 mayinclude a visual sensor, such as a monocular camera, stereoscopiccamera, a red-green-blue (RGB) camera, LIDAR or RADAR. Of course,aspects of the present disclosure are not limited to the aforementionedsensors as other types of sensors, such as, for example, thermal, sonar,and/or lasers are also contemplated for either of the sensors 104, 106.The measurements of the sensors 104, 106 may be processed by one or moreof the processor 120, the sensor module 102, the depth system 108, thecommunication module 122, the location module 118, the locomotion module126, the planning module 124, in conjunction with the computer-readablemedium 114 to implement the functionality described herein. In oneconfiguration, the data captured by the first sensor 104 and the secondsensor 106, may be transmitted to an external device via the transceiver116. The sensors 104, 106 may be coupled to the car 128 or may be incommunication with the car 128.

The location module 118 may be used to determine a location of the car128. For example, the location module 118 may use a global positioningsystem (GPS) to determine the location of the car 128. For example, thevehicle system 100 may be able to communicate with a remote monitoringservice, such as mapping/navigation service, a weather service, or otherenvironmental information provider.

The communication module 122 may be used to facilitate communicationsvia the transceiver 116. For example, the communication module 122 maybe configured to provide communication capabilities via differentwireless protocols, such as Bluetooth, Wi-Fi, long term evolution (LTE),3G, 5G, or the like. The communications module may also be configured toestablish a communication channel between the car 128 and an informationprovider. The communication module 122 may also be used to communicatewith other components of the car 128 that are not modules of the depthsystem 108.

The planning module 124, as well as other modules described herein, maybe software modules running in the processor 120, resident/stored in thecomputer-readable medium 114, one or more hardware modules coupled tothe processor 120, or some combination thereof.

The depth system 108 may be in communication with the sensor module 102,the transceiver 116, the processor 120, the communication module 122,the location module 118, the locomotion module 126, the planning module124, and the computer-readable medium 114. In one configuration, thedepth system 108 may receive sensor data from the sensor module 102. Thesensor module 102 may receive the sensor data from the sensors 104, 106,including images from a monocular camera. According to aspects of thedisclosure, the sensor module 102 may filter the data to remove noise,encode the data, decode the data, merge the data, or perform otherfunctions. In an alternate configuration, the depth system 108 mayreceive sensor data directly from the sensors 104, 106.

As shown in FIG. 1 , the depth system 108 may receive image data fromthe sensor module 102 including, for example, image data from amonocular camera. According to one aspect the depth system 108 mayfunction to process monocular images and provide depth estimates for anenvironment (e.g., objects, surfaces, etc.) depicted therein. Moreover,while depicted as a standalone component, in one or more embodiments,the depth system 108 may be integrated with the locomotion module 126,the sensor module 102, or another module of the vehicle 128. The notedfunctions and methods will become more apparent with a furtherdiscussion of the figures.

The depth system 108, as described herein, may include and processimages according to a depth model to generate one or more depth maps ofthe environment captured by the sensors 104, 106. The depth model may bea self-supervised neural network that trains itself on the image data.The depth system, according to one aspect, use the depth map to generatea number of surface normals indicating vectors perpendicular to asurface perceived in the environment. As described herein, the depth mapmay be segmented and surface normals for each segment may be generated.The depth system may use the surface normals to identify and extract aground plane from the depth map, indicating a horizon, a roadway, orother boundary between the ground and surrounding objects andstructures.

FIG. 2 depicts a diagram of a depth system 108 according to one aspectof the present disclosure. The depth system 108 may include or interfacewith the processor 120. Accordingly, the processor 120 may be a part ofthe depth system 108 or the depth system 108 may access the processor120 through a data bus 130 (FIG. 1 ) or another communication path.According to at least one aspect, the processor 120 may be anapplication-specific integrated circuit (ASIC) that is configured toimplement functions associated with a depth module 220 and an imagemodule 230. In general, the processor 120 may be an electronic processorsuch as a microprocessor that is capable of performing various functionsas described herein. According to one aspect, the depth system 108 mayinclude a memory 210 that stores the depth module 220 and the imagemodule 230. The memory 210 may be a random-access memory (RAM),read-only memory (ROM), a hard disk drive, a flash memory, or othersuitable memory for storing the depth module 220 and image module 230.The depth module 220 and image module 230 may be, for example,computer-readable instructions that when executed by the processor 120cause the processor 120 to perform the various functions disclosedherein.

The depth system 108 may include a data store 240. The data store 240may be, according to one aspect, an electronic data structure stored inthe memory 210 or another data store and may be configured with routinesthat can be executed by the processor 110 for analyzing stored data,providing stored data, organizing stored data, and so on. Thus, in oneembodiment, the data store 240 stores data used by the depth module 220and image module 230 in executing various functions. The data store 240may include monocular image(s) 250, a depth model 260, and a depthmap(s) 270 along with, for example, other information that is used bythe depth module 220 and image module 230.

The monocular images 250 may be, for example, an image from a camerathat encompasses a field-of-view about a vehicle of at least a portionof the surrounding environment. That is, the monocular images 250 maybe, according to one aspect, generally limited to a subregion of thesurrounding environment. As such, the monocular images 250 may be of aforward-facing (i.e., the direction of travel) 60°, 90°, 120°field-of-view, a rear/side facing field-of-view, or some other subregionas defined by the characteristics of the camera. In further aspects, thecamera may be an array of two or more cameras that capture multipleimages of the surrounding environment and stitch the images together toform a comprehensive 360° view of the surrounding environment.

The monocular images 250 may include visual data of the field-of-viewthat is encoded according to an image standard (e.g., codec) associatedwith the camera. In general, characteristics of the camera and the imagestandard may define a format of the monocular images 250. While theparticular characteristics may vary according to differentimplementations, in general, the monocular images 250 may have a definedresolution (i.e., height and width in pixels) and format. Thus, forexample, the monocular images 250 may be generally an RGB visible lightimage. In further aspects, the monocular images 250 may be infraredimages associated with a corresponding infrared camera, a black/whiteimage, or another suitable format as may be desired. Whichever formatthat the depth system 108 implements, the monocular images 250 may be amonocular in that there is no explicit additional modality indicatingdepth. In contrast to a stereo image that may integrate left and rightimages from separate cameras mounted side-by-side, the monocular images250 may not include explicit depth information such as disparity mapsderived from comparing the stereo images pixel-by-pixel.

Instead, the monocular images 250 may provide depth informationimplicitly in the relationships of perspective and size of elementsdepicted in the monocular images 250 from which the depth module 220derives the depth map 270 by using the depth model 260. The depth map270, according to one aspect, may be a data structure corresponding tothe monocular images 250 that indicate distances or depths to objectsand features represented in the monocular images 250.

The depth module 220 generally may employ the depth model 260 to producethe depth map 270 as an inverse mapping having inverse values for thedepth estimates. That is, instead of providing plain depth data, thedepth module 220 may implement the depth model 260 to provide the depthestimates in an inverse form. Thus, depth module 220 may subsequentlyinvert the values of the depth map 270 to provide the depth values orthe image module 230 may separately invert the depth map 270 to generatedepth estimates. Moreover, the depth module 220 may also selectivelyoutput the depth map 270 from the depth model 260 at different points inprocessing in order to provide the depth map 270 at different scales.

The depth module 220 may provide, for example, the depth map 270 at thedifferent scales separately to different systems in the car 128 via theimage module 230. That is, separate systems may function on finer orcoarser resolutions of depth information depending on a particular taskthat is undertaken. Accordingly, the depth module 220 can separatelyprovide the different scales to the various systems and/or omitsubsequent processing where a fuller resolution form of the depth map270 is not required. As such, the depth module 220 generally provides arobust implementation for resolving depth estimates that can be adaptedto different systems.

According to one aspect, the image module 230 generally may includeinstructions that function to control the processor 120 to executevarious actions in support of the depth module 220. For example, theimage module 230 may receive the monocular images 250 from the cameraand provide the monocular images 250 to the depth module 220. The imagemodule 230 may receive the monocular images 250 by controlling thecamera to capture the monocular images 250, by passively acquiring themonocular images 250 from a data bus or electronic memory, or the like.The image module 230 may also perform pre-processing on the monocularimages 250 to provide the monocular images 250 in a format that isaccepted by the depth model 260.

According to one aspect, the image module 230 may handle outputs fromthe depth module 220 and depth model 260. The image module 230 mayinclude instructions to, for example, perform one or morepost-processing routines, including extracting a ground plane, providingthe depth map 270 to additional systems/modules in the car 128 in orderto control the operation of the modules and/or the car 128 overall, andso on. In still further aspects, the image module 230 may communicatethe depth map 270 to a remote system (e.g., cloud-based system) as, forexample, a mechanism for mapping the surrounding environment or forother purposes (e.g., traffic reporting, etc.). As one example, theimage module 230 may use the depth map 270 to map locations of obstaclesin the surrounding environment and plan a trajectory that safelynavigates the obstacles. Thus, the image module 230 may use the depthmap 270 to control the car 128 to navigate through the surroundingenvironment.

In further aspects, the image module 230 may convey the depth map 270 tofurther internal systems/components of the car 128 such as thelocomotion module 126. For example, the image module 230 may acquire thedepth map 270 and convey the depth map 270 to the locomotion module 126in a particular scale that the locomotion module 126 accepts as anelectronic input. In this way, the depth system 170 may inform thelocomotion module 126 of the depth estimates to improve situationalawareness and planning of the locomotion module 126. As such, thelocomotion module 126 may acquire the depth map 270 as a primary sourceof depth information for the surrounding environment or as a secondarysource that verifies other image capture and processing capabilities(e.g., LiDAR data). It should be appreciated that the locomotion module126 is indicated as one example, and, in further arrangements, the imagemodule 230 may provide the depth map 270 to the locomotion module 126and/or other components in parallel or as a separate conveyance.

According to one aspect of the present disclosure, the image module 230includes instructions to train the depth model 260. The image module230, may train the depth model 260 using a self-supervised structurefrom motion (SfM) process. Thus, to initiate the depth model 260, theimage module 230 may use images from video of a scene and formulate thegeneration of the depth map 270 as a photometric error minimizationacross the images. In general, the image module 230 may train the depthmodel 260 by causing the depth module 220 to execute the depth model 260as though typical operation is underway, however, the image module 230may provide one or more images from the video as the monocular images250 for processing.

As such, the image module 230 may use the resulting map 270 to identifyand extract a ground plane from the surrounding environment via thegenerated depth maps. According to one aspect, the depth map 270 mayreflect one or more surfaces or one or more structures perceived in theenvironment. FIG. 3 depicts a conceptual diagram of a three-dimensionalscene 300, as perceived by a car 128, transformed to a depth map 300′with calculated surface normals 314 according to one aspect of thepresent disclosure.

As one example, according to an aspect of the present disclosure, thecar 128 may be traveling along a roadway 302 that is surrounded by oneor more structures 304, 306, 308, 310. A monocular camera on the car 128may capture a monocular image of the scene 300 for processing by thedepth system 108, as described herein. The depth system 108 may output adepth map 300′ as a representative reconstruction of the scene 300. Thedepth map 300′ may include one or more transformed structures 304′,306′, 308′ 310′. The depth system 108 may further segment the depth mapinto one or more polygons, such as triangles 312. Surface normals 314from each triangle 312 may be generated indicating a directionperpendicular to the various surfaces on which the triangles are mapped.

Surface normals 314 may be generated from each triangle using across-product operation. A surface normal 314 for a triangle may becalculated by taking the vector cross product of two edges of thattriangle. For example, a triangle 312 may be defined by points p₁, p₂,p₃, if the vector U=p₂−p₁ and the vector V=p₃−p₁ then the normal N=U×Vand can be calculated by:N _(x) =U _(y) V _(z) −U _(z) V _(y)N _(y) =U _(z) V _(x) −U _(x) V _(z)N _(z) =U _(x) V _(y) −U _(y) V _(x)

The surface normals 314 are perpendicular to the triangles 312,therefore if the surface normal vectors of neighboring triangles 312 arepointing in the same direction, it may be reasoned that the triangles312 are part of the same surface. Further, if the ground plane isassumed to be generally horizontal, any surface normals directedstraight upwards may be indicative of the ground plane, and thus, atleast partially, the perceived roadway 302′. Surface normals 312 of theone or more transformed structures 304′, 306′, 308′ 310′, indicated as a‘+’, reflect a surface perpendicular to the camera and/or car 128.

One skilled in the art will recognize the features depicted in FIG. 3are simplified for the purpose of explanation. That is, the entire depthmap 300′ may be segmented into a number of contiguous polygons, ortriangles, and surface normals may be generated for each polygon.Further, one of skill in the art will recognize that the depth map 300′of FIG. 3 is simplified to reflect only two surfaces (i.e., the groundplane and a number of perpendicular structures), however aspects of thepresent disclosure include more complex geometries and dimensions asthey may be imaged by the camera and processed by the depth system 108.

According to one aspect of the present disclosure, the calculatedsurface normals 314 generally pointing upwards may be indicative of theground plane. According to one aspect, a tolerance or range of degreesof the surface normals may be implemented to account for variations inthe ground surface, such as hills or other bumps. Additionally, thedepth system 108 may use a similar methodology to identify multipleplanes representing irregular or hilly roads. According to one aspect, aheight map may be generated representing multiple planes yielding aterrain map of the entire environment.

Referring back to FIG. 2 , the image module 230 may then use the depthmap and ground plane data thereby training in a self-supervising mannerthe depth model 260 to produce improved depth estimates and groundplanes without requiring semantic labeling. According to one aspect, thedepth model and depth system 108 may be trained in a self-supervisedmanner without the input of training labels or other semanticinformation. Using only surface normal information from a depth map, theidentification of one or more ground planes may be accomplished withoutany supervisory depth or semantic information. In general, the imagemodule 230 may train the depth model 260 over a training data set ofmonocular video sequences that are generally comprised of many separateimages. According to one aspect of the present disclosure, one aim ofdepth and ego-motion self-supervised training may be to learngeneralizable features, such as ground plane identification, that may beused in other settings to estimate depth and ego-motion from cameraimages.

Through this training process, the depth model 260 may develop a learnedprior of the monocular images 250 as embodied by the internal parametersof the depth model 260 from the training on the images. In general, thedepth model 260 develops the learned understanding about how depthrelates to various aspects of an image according to, for example, size,perspective, and so on. Consequently, the resulting trained depth model260 is leveraged by the depth system 108 to estimate depths frommonocular images that do not include an explicit modality identifyingthe depths.

FIG. 4 depicts a method 400 of extracting a ground plane within asurrounding environment from monocular images. As described herein, andshown in block 402, the depth system may receive one or more monocularimages from an image sensor or image capture device of an environment ofinterest. The depth system may, as shown in block 404, extract one ormore feature maps from the monocular images according to a depth model,described herein. The depth model may be, for example, a convolutionalneural network in the form of an encoder/decoder architecture. Theoutput of the convolutional neural network, including the feature maps,may be used to generate a depth map, as shown in bock 406 and describedherein.

According to one aspect, as shown in block 408, the depth system mayextract surface normal information from the depth map. As describedherein, the surface normals may be generated from defined polygons, suchas triangles, in the depth map. Surface normal vectors of neighboringpolygons facing the same direction may be expected to be part of thesame surface, structure or object in the original image. As shown inblock 410, the surface normals may be used to extract a ground planefrom the depth map. For example, assuming that the ground issubstantially horizontal, a number of surface normals all facingsubstantially the same direction and facing straight upwards, mayindicate a ground surface, from which a ground plane may be extracted.As described herein, multiple planes may be identified using similarmethods to reflect a change in elevation of the ground surface, or togenerate a height map representing the entire terrain of theenvironment.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the present disclosure is intended to cover any aspect ofthe present disclosure, whether implemented independently of or combinedwith any other aspect of the present disclosure. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth. In addition, the scope of the presentdisclosure is intended to cover such an apparatus or method practicedusing other structure, functionality, or structure and functionality inaddition to, or other than the various aspects of the present disclosureset forth. It should be understood that any aspect of the presentdisclosure may be embodied by one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the presentdisclosure. Although some benefits and advantages of the preferredaspects are mentioned, the scope of the present disclosure is notintended to be limited to particular benefits, uses or objectives.Rather, aspects of the present disclosure are intended to be broadlyapplicable to different technologies, system configurations, networksand protocols, some of which are illustrated by way of example in thefigures and in the following description of the preferred aspects. Thedetailed description and drawings are merely illustrative of the presentdisclosure rather than limiting, the scope of the present disclosurebeing defined by the appended claims and equivalents thereof.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a processor specially configured to perform the functionsdiscussed in the present disclosure. The processor may be a neuralnetwork processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate arraysignal (FPGA) or other programmable logic device (PLD), discrete gate ortransistor logic, discrete hardware components or any combinationthereof designed to perform the functions described herein.Alternatively, the processing system may comprise one or moreneuromorphic processors for implementing the neuron models and models ofneural systems described herein. The processor may be a microprocessor,controller, microcontroller, or state machine specially configured asdescribed herein. A processor may also be implemented as a combinationof computing devices, e.g., a combination of a DSP and a microprocessor,a plurality of microprocessors, one or more microprocessors inconjunction with a DSP core, or such other special configuration, asdescribed herein.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in storage or machine readable medium,including random access memory (RAM), read only memory (ROM), flashmemory, erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), registers, a hard disk,a removable disk, a CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.A software module may comprise a single instruction, or manyinstructions, and may be distributed over several different codesegments, among different programs, and across multiple storage media. Astorage medium may be coupled to a processor such that the processor canread information from, and write information to, the storage medium. Inthe alternative, the storage medium may be integral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and processing,including the execution of software stored on the machine-readablemedia. Software shall be construed to mean instructions, data, or anycombination thereof, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or specialized register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The machine-readable media may comprise a number of software modules.The software modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RANI from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a specialpurpose register file for execution by the processor. When referring tothe functionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any storage medium that facilitatestransfer of a computer program from one place to another.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means, such that a user terminal and/or basestation can obtain the various methods upon coupling or providing thestorage means to the device. Moreover, any other suitable technique forproviding the methods and techniques described herein to a device can beutilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

The invention claimed is:
 1. A system for generating a ground plane ofan environment, comprising: one or more processors; a memorycommunicably coupled to the one or more processors and storing: a depthsystem including instructions that when executed by the one or moreprocessors cause the one or more processors to generate a plurality ofdepth maps by: receiving at least one monocular image; processing the atleast one monocular image according to a depth model and a first andsecond scale; output a first depth map at the first scale and a seconddepth map at the second scale; and an image module includinginstructions that when executed by the one or more processors cause theone or more processors to: define in the first and second depth maps aplurality of polygons; for each polygon generate a surface normal;extract the ground plane from the surface normal for each polygon; andtransmit the first depth map to a first vehicle system and the seconddepth map to a second vehicle system.
 2. The system of claim 1 whereinthe plurality of polygons comprises a plurality of triangles.
 3. Thesystem of claim 2 wherein each surface normal is generated using a crossproduct of the triangle.
 4. The system of claim 1 wherein extracting theground plane includes identifying a plurality of surface normals facingsubstantially the same direction.
 5. The system of claim 4 whereinsubstantially the same direction includes an upward direction.
 6. Thesystem of claim 1 wherein the depth model comprises a neural network. 7.The system of claim 6 wherein the neural network is self-supervised. 8.A method for generating a ground plane of an environment, comprising:receiving at least one monocular image; generating a plurality of depthmaps by processing the at least one monocular image according to a depthmodel and a first and second scale; outputting a first depth map at thefirst scale and a second depth map at the second scale; defining in thefirst and second depth maps a plurality of polygons; for each polygon,generating a surface normal; extracting a ground plane from the surfacenormal for each polygon; and transmit the first depth map to a firstvehicle system and the second depth map to a second vehicle system. 9.The method of claim 8 wherein the plurality of polygons comprises aplurality of triangles.
 10. The method of claim 9 wherein each surfacenormal is generated using a cross product of the triangle.
 11. Themethod of claim 9 wherein extracting the ground plane includesidentifying a plurality of surface normals facing substantially the samedirection.
 12. The method of claim 11 wherein substantially the samedirection includes an upward direction.
 13. The method of claim 8wherein the depth model comprises a neural network.
 14. The method ofclaim 13 wherein the neural network is self-supervised.
 15. Anon-transitory computer-readable medium for generating a ground planefor an environment and including instructions that when executed by oneor more processors cause the one or more processors to: receive at leastone monocular image; generate a plurality of depth maps by processingthe at least one monocular image according to a depth model and a firstand second scale; output a first depth map at the first scale and asecond depth map at the second scale; define in the first and seconddepth maps a plurality of polygons; for each polygon, generate a surfacenormal; extract a ground plane from the surface normal for each polygon;and transmit the first depth map to a first vehicle system and thesecond depth map to a second vehicle system.
 16. The non-transitorycomputer-readable medium of claim 15 wherein the plurality of polygonscomprises a plurality of triangles.
 17. The non-transitorycomputer-readable medium of claim 16 wherein each surface normal isgenerated using a cross product of the triangle.
 18. The non-transitorycomputer-readable medium of claim 16 wherein substantially the samedirection includes an upward direction.
 19. The non-transitorycomputer-readable medium of claim 15 wherein extracting the ground planeincludes identifying a plurality of surface normals facing substantiallythe same direction.
 20. The non-transitory computer-readable medium ofclaim 15 wherein the depth model comprises a self-supervised neuralnetwork.